{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "### Interactive Distribution Transformations in Python \n",
    "\n",
    "\n",
    "#### Michael Pyrcz, Professor, The University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering with Distribution Transformations\n",
    "\n",
    "Here are 2 interactive workflows demonstrationing distribution transformations, a common, essential tool for feature engineering for predictive machine learning.\n",
    "\n",
    "I have recorded a walk-through of this interactive dashboard in my [Data Science Interactive Python Demonstrations](https://www.youtube.com/playlist?list=PLG19vXLQHvSDy26fM3hDLg3VCU7U5BGZl) series on my [YouTube](https://www.youtube.com/@GeostatsGuyLectures) channel.\n",
    "\n",
    "* Join me for walk-through of this dashboard [04 Data Science Interactive: Distribution Transformations](TBD). I'm stoked to guide you and share observations and things to try out!   \n",
    "\n",
    "* I have a lecture on [Distribution Transformation](https://www.youtube.com/watch?v=ZDIpE3OkAIU&list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ&index=14) as part of my [Data Analytics and Geostatistics](https://www.youtube.com/playlist?list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ) course. Note, for all my recorded lecture the interactive and well-documented workflow demononstrations are available on my GitHub repository [GeostatsGuy's Python Numerical Demos](https://github.com/GeostatsGuy/PythonNumericalDemos).\n",
    "\n",
    "* Also, I have a lecture on [Feature Transformations for Machine Learning](https://www.youtube.com/watch?v=6QJjZoWknEI&list=PLG19vXLQHvSC2ZKFIkgVpI9fCjkN38kwf&index=9) as part of my [Machine Learning](https://www.youtube.com/playlist?list=PLG19vXLQHvSC2ZKFIkgVpI9fCjkN38kwf) course.\n",
    "\n",
    "* Finally, I have a lecture on [Q-Q plots](https://www.youtube.com/watch?v=RETZus4XBNM&list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ&index=23)  as part of my [Data Analytics and Geostatistics](https://www.youtube.com/playlist?list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ) course.\n",
    "\n",
    "#### Distribution Transformations Motivation\n",
    "\n",
    "With distribution transformation we map our feature to a new feature with a new distribution, e.g., we could transform our sample data to be Gaussian distributed. \n",
    "\n",
    "Why do we do this? Here are some reasons for distribution transformation in data science:\n",
    "\n",
    "* **Inference**: the feature distribution has expected shape from theory, so we are adding important information\n",
    "\n",
    "* **Data Preparation / Cleaning**: correcting for too data paucity, data noise and data outliers\n",
    "\n",
    "* **Theory**: a specific distribution assumption is required for a method, so we correct the data to have this distribution to to avoid invalidating an assumption\n",
    "\n",
    "#### Distribution Transformations\n",
    "\n",
    "How do we do it? We apply the following to all sample data, $x_{\\alpha}$ $\\forall$ $\\alpha = 1,\\ldots,n$.\n",
    "\n",
    "\\begin{equation}\n",
    "y_{\\alpha} = G^{-1}_Y\\left(F_X(x_{\\alpha})\\right)\n",
    "\\end{equation}\n",
    "\n",
    "were $X$ is the original feature with a $F_X$ original cumulative distribution function and $Y$ is transformed feature with a $G_Y$ transformed cumulative distribution function.\n",
    "\n",
    "Let's summarize this operation:\n",
    "\n",
    "* Mapping from one distribution to another through percentiles\n",
    "\n",
    "* This may be applied to any parametric or nonparametric distributions\n",
    "\n",
    "* This is a rank preserving transform, e.g. P50 of 𝑋 is P50 of 𝑌\n",
    "\n",
    "#### Distribution Transformation Dashboards\n",
    "\n",
    "To demonstrate distributions, I built out 2 dashboards:\n",
    "\n",
    "1. data to a nonparametric distribution (to match the distribution of another dataset)\n",
    "2. data to a parametric distribution (to a Gaussian distribution)\n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "\n",
    "That's all!\n",
    "\n",
    "#### Load the Required Libraries\n",
    "\n",
    "We will also need some standard Python packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                        # ndarrys for gridded data\n",
    "import pandas as pd                       # DataFrames for tabular data\n",
    "import matplotlib.pyplot as plt           # plotting\n",
    "from scipy import stats                   # summary statistics\n",
    "import math                               # trigonometry etc.\n",
    "import random                             # randon numbers\n",
    "from scipy.stats import norm              # Gaussian parametric distribution\n",
    "import geostatspy.GSLIB as GSLIB\n",
    "from ipywidgets import interactive        # widgets and interactivity\n",
    "from ipywidgets import widgets                            \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import Label\n",
    "from ipywidgets import VBox, HBox\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')         # supress warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the Random Number Seed\n",
    "\n",
    "Set the random number seed so that we have a repeatable workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 73073"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object.  For fun try misspelling the name. You will get an ugly, long error.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_url = \"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data.csv\"\n",
    "df = pd.read_csv(data_url)     # load our data table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It worked, we loaded our file into our DataFrame called 'df'. But how do you really know that it worked? Visualizing the DataFrame would be useful and we already leard about these methods in this demo (https://git.io/fNgRW). \n",
    "\n",
    "We can preview the DataFrame by printing a slice or by utilizing the 'head' DataFrame member function (with a nice and clean format, see below). With the slice we could look at any subset of the data table and with the head command, add parameter 'n=13' to see the first 13 rows of the dataset.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100187</td>\n",
       "      <td>1.363890</td>\n",
       "      <td>5110.699751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.107947</td>\n",
       "      <td>12.576845</td>\n",
       "      <td>4671.458560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.085357</td>\n",
       "      <td>5.984520</td>\n",
       "      <td>6127.548006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.108460</td>\n",
       "      <td>2.446678</td>\n",
       "      <td>5201.637996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.102468</td>\n",
       "      <td>1.952264</td>\n",
       "      <td>3835.270322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.110579</td>\n",
       "      <td>3.691908</td>\n",
       "      <td>5295.267191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       X      Y  Facies  Porosity       Perm           AI\n",
       "0  100.0  900.0     1.0  0.100187   1.363890  5110.699751\n",
       "1  100.0  800.0     0.0  0.107947  12.576845  4671.458560\n",
       "2  100.0  700.0     0.0  0.085357   5.984520  6127.548006\n",
       "3  100.0  600.0     0.0  0.108460   2.446678  5201.637996\n",
       "4  100.0  500.0     0.0  0.102468   1.952264  3835.270322\n",
       "5  100.0  400.0     0.0  0.110579   3.691908  5295.267191"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(n=6)                           # we could also use this command for a table preview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating and Plotting a CDF by Hand\n",
    "\n",
    "Let's demonstrate the calculation and plotting of a non-parametric CDF by hand\n",
    "\n",
    "1. make a copy of the feature as a 1D array (ndarray from NumPy)\n",
    "2. sort the data in ascending order\n",
    "3. assign cumulative probabilities based on the tails assumptions\n",
    "4. plot cumulative probability vs. value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ndarray has a shape of (261,).\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "por = df['Porosity'].copy(deep = True).values # make a deepcopy of the feature from the DataFrame\n",
    "print('The ndarray has a shape of ' + str(por.shape) + '.')\n",
    "\n",
    "por = np.sort(por)                           # sort the data in ascending order\n",
    "n = por.shape[0]                             # get the number of data samples\n",
    "\n",
    "cprob = np.zeros(n)\n",
    "for i in range(0,n):\n",
    "    index = i + 1\n",
    "    cprob[i] = index / n                     # known upper tail\n",
    "    # cprob[i] = (index - 1)/n               # known lower tail\n",
    "    # cprob[i] = (index - 1)/(n - 1)         # known upper and lower tails\n",
    "    # cprob[i] = index/(n+1)                 # unknown tails  \n",
    "\n",
    "plt.subplot(111)\n",
    "plt.plot(por,cprob, alpha = 0.2, c = 'black') # plot piecewise linear interpolation\n",
    "plt.scatter(por,cprob,s = 10, alpha = 1.0, c = 'red', edgecolor = 'black') # plot the CDF points\n",
    "plt.grid(); plt.xlim([0.05,0.25]); plt.ylim([0.0,1.0])\n",
    "plt.xlabel(\"Porosity (fraction)\"); plt.ylabel(\"Cumulative Probability\"); plt.title(\"Non-parametric Porosity Cumulative Distribution Function\")\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.2, wspace=0.1, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution Transformation to a Parametric Distribution\n",
    "\n",
    "We can transform our data feature distribution to any parametric distribution with this workflow.\n",
    "\n",
    "1. Calculate the cumulative probability value of each of our data values, $p_{\\alpha} = F_x(x_\\alpha)$, $\\forall$ $\\alpha = 1,\\ldots, n$.\n",
    "\n",
    "2. Apply the inverse of the target parametric cumulative distribution function (CDF) to calculate the transformed values. $y_{\\alpha} = G_y^{-1}\\left(F_x(x_\\alpha)\\right)$, $\\forall$ $\\alpha = 1,\\ldots, n$.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.zeros(n)\n",
    "\n",
    "for i in range(0,n):\n",
    "    y[i] = norm.ppf(cprob[i],loc=0.0,scale=1.0)\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(por,cprob, alpha = 0.2, c = 'black') # plot piecewise linear interpolation\n",
    "plt.scatter(por,cprob,s = 10, alpha = 1.0, c = 'red', edgecolor = 'black') # plot the CDF points\n",
    "plt.grid(); plt.xlim([0.05,0.25]); plt.ylim([0.0,1.0])\n",
    "plt.xlabel(\"Porosity (fraction)\"); plt.ylabel(\"Cumulative Probability\"); plt.title(\"Non-parametric Porosity Cumulative Distribution Function\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(y,cprob, alpha = 0.2, c = 'black') # plot piecewise linear interpolation\n",
    "plt.scatter(y,cprob,s = 10, alpha = 1.0, c = 'red', edgecolor = 'black') # plot the CDF points\n",
    "plt.grid(); plt.xlim([-3.0,3.0]); plt.ylim([0.0,1.0])\n",
    "plt.xlabel(\"Porosity (fraction)\"); plt.ylabel(\"Cumulative Probability\"); plt.title(\"After Distribution Transformation to Gaussian\")\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make an interactive version of this plot to visualize the transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# widgets and dashboard\n",
    "l = widgets.Text(value='                                                  Data Analytics, Distribution Transformation, Prof. Michael Pyrcz, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "\n",
    "data_index = widgets.IntSlider(min=1, max = n-1, value=1.0, step = 10.0, description = 'Data Index, $\\\\alpha$',orientation='horizontal', style = {'description_width': 'initial'}, continuous_update=False)\n",
    "\n",
    "ui = widgets.VBox([l,data_index],)\n",
    "\n",
    "def run_plot(data_index):                       # make data, fit models and plot\n",
    "    plt.subplot(131)\n",
    "    plt.plot(por,cprob, alpha = 0.2, c = 'black') # plot piecewise linear interpolation\n",
    "    plt.scatter(por,cprob,s = 10, alpha = 1.0, c = 'red', edgecolor = 'black') # plot the CDF points\n",
    "    plt.grid(); plt.xlim([0.05,0.25]); plt.ylim([0.0,1.0])\n",
    "    plt.xlabel(\"Original Feature, $x$\"); plt.ylabel(\"Cumulative Probability\"); plt.title(\"Original Cumulative Distribution Function\")\n",
    "    plt.plot([por[data_index-1],por[data_index-1]],[0.0,cprob[data_index-1]],color = 'red',linestyle='dashed')\n",
    "    plt.plot([por[data_index-1],3.0],[cprob[data_index-1],cprob[data_index-1]],color = 'red',linestyle='dashed')\n",
    "    plt.annotate('x = ' + str(round(por[data_index-1],2)), xy=(por[data_index-1]+0.003, 0.01))\n",
    "    plt.annotate('p = ' + str(round(cprob[data_index-1],2)), xy=(0.225, cprob[data_index-1]+0.02))\n",
    "\n",
    "    \n",
    "    plt.subplot(132)\n",
    "    plt.plot(y,cprob, alpha = 0.2, c = 'black') # plot piecewise linear interpolation\n",
    "    plt.scatter(y,cprob,s = 10, alpha = 1.0, c = 'red', edgecolor = 'black') # plot the CDF points\n",
    "    plt.grid(); plt.xlim([-3.0,3.0]); plt.ylim([0.0,1.0])\n",
    "    plt.xlabel(\"Gaussian Transformed Feature, $y$\"); plt.ylabel(\"Cumulative Probability\"); plt.title(\"After Distribution Transformation to Gaussian\")\n",
    "    plt.plot([-3.0,y[data_index-1]],[cprob[data_index-1],cprob[data_index-1]],color = 'red',linestyle='dashed')\n",
    "    plt.plot([y[data_index-1],y[data_index-1]],[0.0,cprob[data_index-1]],color = 'red',linestyle='dashed')\n",
    "    #plt.arrow(y[data_index-1],cprob[data_index-1],0.0,-1.0*(cprob[data_index-1]-0.01),color = 'red',width = 0.02, head_width = 0.1, linestyle='dashed', head_length = 0.01)\n",
    "    plt.annotate('p = ' + str(round(cprob[data_index-1],2)), xy=(-2.90, cprob[data_index-1]+0.02)) \n",
    "    plt.annotate('y = ' + str(round(y[data_index-1],2)), xy=(y[data_index-1]+0.1, 0.01))\n",
    "    \n",
    "    plt.subplot(133)\n",
    "    plt.plot(por,y, alpha = 0.2, c = 'black') # plot piecewise linear interpolation\n",
    "    plt.grid(); plt.xlim([0.05,0.25]); plt.ylim([-3.0,3.0])\n",
    "    plt.xlabel(\"Original Porosity (fraction)\"); plt.ylabel(\"Gaussian Transformed Porosity (N[fraction])\"); plt.title(\"Parametric Distribution Transformation, Q-Q Plot\")\n",
    "    #plt.plot([0.05,0.25],[0.05,0.25],color = 'red',linestyle='dashed', alpha = 0.4)\n",
    "    plt.scatter(por[data_index-1],y[data_index-1],s = 50, c = 'red', edgecolor = 'black', alpha = 1.0, zorder=200) # plot the CDF points\n",
    "    plt.scatter(por,y,s = 20, c = 'red', edgecolor = 'black', alpha = 0.1, zorder=100) # plot the CDF points\n",
    "   \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "    plt.show()\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(run_plot, {'data_index':data_index})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Data Analytics Distribution Transformation Demonstration \n",
    "\n",
    "#### Michael Pyrcz, Professor, The University of Texas at Austin \n",
    "\n",
    "Select any data value and observe the distribution transform by mapping through cumulative probability.\n",
    "\n",
    "### The Inputs\n",
    "\n",
    "* **data_index** - the data index from 1 to n in the sorted ascending order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5dfaa0890fc455ba201e3cff049630d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                                                  Data Analytics, Distribution Tran…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2cd19736d0c4a129e3715889f783f3e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 640x480 with 3 Axes>', 'i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui, interactive_plot)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution Transform to a Non-Parametric Distribution\n",
    "\n",
    "We can apply the mapping through cumulative probabilities to transform from any distribution to any other distribution.\n",
    "\n",
    "* let's make a new data set by randomly sampling from the previous one and adding error\n",
    "\n",
    "Then we can demonstrate transforming this dataset to match the original distribution\n",
    "\n",
    "* this is mimicking the situation where we transform a dataset to match the distribution of a better sampled analog distribution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sample ndarray has a shape of (30,).\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_sample = 30\n",
    "df_sample = df.sample(n_sample,random_state = seed)\n",
    "                      \n",
    "df_sample = df_sample.copy(deep = True) # make a deepcopy of the feature from the DataFrame\n",
    "\n",
    "df_sample['Porosity'] = df_sample['Porosity'].values + np.random.normal(loc = 0.0, scale = 0.01, size = n_sample)\n",
    "\n",
    "df_sample = df_sample.sort_values(by = 'Porosity')                # sort the DataFrame\n",
    "por_sample = df_sample['Porosity'].values\n",
    "print('The sample ndarray has a shape of ' + str(por_sample.shape) + '.')\n",
    "\n",
    "cprob_sample = np.zeros(n_sample)\n",
    "for i in range(0,n_sample):\n",
    "    index = i + 1\n",
    "    cprob_sample[i] = index / n_sample       # known upper tail\n",
    "    # cprob[i] = (index - 1)/n               # known lower tail\n",
    "    # cprob[i] = (index - 1)/(n - 1)         # known upper and lower tails\n",
    "    # cprob[i] = index/(n+1)                 # unknown tails  \n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(por_sample,cprob_sample, alpha = 0.2, c = 'black') # plot piecewise linear interpolation\n",
    "plt.scatter(por_sample,cprob_sample,s = 10, alpha = 1.0, c = 'red', edgecolor = 'black') # plot the CDF points\n",
    "plt.grid(); plt.xlim([0.05,0.25]); plt.ylim([0.0,1.0])\n",
    "plt.xlabel(\"Porosity (fraction)\"); plt.ylabel(\"Cumulative Probability\"); plt.title(\"Sparse Sample with Noise Cumulative Distribution Function\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(por,cprob, alpha = 0.2, c = 'black') # plot piecewise linear interpolation\n",
    "plt.scatter(por,cprob,s = 10, alpha = 1.0, c = 'red', edgecolor = 'black') # plot the CDF points\n",
    "plt.grid(); plt.xlim([0.05,0.25]); plt.ylim([0.0,1.0])\n",
    "plt.xlabel(\"Porosity (fraction)\"); plt.ylabel(\"Cumulative Probability\"); plt.title(\"Non-parametric Porosity Cumulative Distribution Function\")\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's transform the values and show them on the target distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_sample = np.zeros(n_sample)\n",
    "\n",
    "for i in range(0,n_sample):\n",
    "    y_sample[i] = np.percentile(por,cprob_sample[i]*100, interpolation = 'linear') # piecewise linear interpolation of inverse of target CDF \n",
    "    \n",
    "plt.subplot(121)\n",
    "plt.plot(por_sample,cprob_sample, alpha = 0.2, c = 'black') # plot piecewise linear interpolation\n",
    "plt.scatter(por_sample,cprob_sample,s = 30, alpha = 1.0, c = 'green', edgecolor = 'black', zorder = 100) # plot the CDF points\n",
    "plt.grid(); plt.xlim([0.05,0.25]); plt.ylim([0.0,1.0])\n",
    "plt.xlabel(\"Porosity (fraction)\"); plt.ylabel(\"Cumulative Probability\"); plt.title(\"Sparse Sample with Noise Cumulative Distribution Function\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(por,cprob, alpha = 0.2, c = 'black') # plot piecewise linear interpolation\n",
    "plt.scatter(por,cprob,s = 10, c = 'red', edgecolor = 'black', alpha = 0.3) # plot the CDF points\n",
    "plt.scatter(y_sample,cprob_sample,s = 30, c = 'green', edgecolor = 'black', alpha = 1.0, zorder = 100) # plot the CDF points\n",
    "plt.grid(); plt.xlim([0.05,0.25]); plt.ylim([0.0,1.0])\n",
    "plt.xlabel(\"Porosity (fraction)\"); plt.ylabel(\"Cumulative Probability\"); plt.title(\"Non-parametric Porosity Cumulative Distribution Function\")\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make an interactive version of this plot to visualize the transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# widgets and dashboard\n",
    "l_sample = widgets.Text(value='                                                  Data Analytics, Distribution Transformation, Prof. Michael Pyrcz, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "\n",
    "data_index_sample = widgets.IntSlider(min=1, max = n_sample, value=1.0, step = 1.0, description = 'Data Sample Index, $\\\\beta$',orientation='horizontal', style = {'description_width': 'initial'}, continuous_update=False)\n",
    "\n",
    "ui_sample = widgets.VBox([l_sample,data_index_sample],)\n",
    "\n",
    "def run_plot_sample(data_index_sample):                       # make data, fit models and plot\n",
    "    plt.subplot(131)\n",
    "    plt.plot(por_sample,cprob_sample, alpha = 0.2, c = 'black') # plot piecewise linear interpolation\n",
    "    plt.scatter(por_sample,cprob_sample,s = 30, alpha = 1.0, c = 'green', edgecolor = 'black',zorder = 100) # plot the CDF points\n",
    "    plt.grid(); plt.xlim([0.05,0.25]); plt.ylim([0.0,1.0])\n",
    "    plt.xlabel(\"Porosity (fraction)\"); plt.ylabel(\"Cumulative Probability\"); plt.title(\"Original Sparse Sample with Noise, Cumulative Distribution Function\")\n",
    "    plt.plot([por_sample[data_index_sample-1],por_sample[data_index_sample-1]],[0.0,cprob_sample[data_index_sample-1]],color = 'red',linestyle='dashed')\n",
    "    plt.plot([por_sample[data_index_sample-1],3.0],[cprob_sample[data_index_sample-1],cprob_sample[data_index_sample-1]],color = 'red',linestyle='dashed')\n",
    "    plt.annotate('x = ' + str(round(por_sample[data_index_sample-1],2)), xy=(por_sample[data_index_sample-1]+0.003, 0.01))\n",
    "    plt.annotate('p = ' + str(round(cprob_sample[data_index_sample-1],2)), xy=(0.225, cprob_sample[data_index_sample-1]+0.02))\n",
    "    \n",
    "    plt.subplot(132)\n",
    "    plt.plot(por,cprob, alpha = 0.2, c = 'black') # plot piecewise linear interpolation\n",
    "    plt.scatter(por,cprob,s = 10, c = 'red', edgecolor = 'black', alpha = 1.0) # plot the CDF points\n",
    "    plt.grid(); plt.xlim([0.05,0.25]); plt.ylim([0.0,1.0])\n",
    "    plt.xlabel(\"Porosity (fraction)\"); plt.ylabel(\"Cumulative Probability\"); plt.title(\"Non-parametric Target Porosity Cumulative Distribution Function\")\n",
    "    plt.plot([0.0,y_sample[data_index_sample-1]],[cprob_sample[data_index_sample-1],cprob_sample[data_index_sample-1]],color = 'red',linestyle='dashed')\n",
    "    plt.plot([y_sample[data_index_sample-1],y_sample[data_index_sample-1]],[0.0,cprob_sample[data_index_sample-1]],color = 'red',linestyle='dashed')\n",
    "    plt.annotate('p = ' + str(round(cprob_sample[data_index_sample-1],2)), xy=(0.053, cprob_sample[data_index_sample-1]+0.02)) \n",
    "    plt.annotate('y = ' + str(round(y_sample[data_index_sample-1],2)), xy=(y_sample[data_index_sample-1]+0.003, 0.01))\n",
    "    plt.scatter(y_sample[data_index_sample-1],cprob_sample[data_index_sample-1],s = 50, c = 'green', edgecolor = 'black', alpha = 1.0, zorder=100) # plot the CDF points\n",
    "    \n",
    "    plt.subplot(133)\n",
    "    plt.plot(por_sample,y_sample, alpha = 0.2, c = 'black') # plot piecewise linear interpolation\n",
    "    plt.grid(); plt.xlim([0.05,0.25]); plt.ylim([0.05,0.25])\n",
    "    plt.xlabel(\"Original Porosity (fraction)\"); plt.ylabel(\"Transformed Porosity (fraction)\"); plt.title(\"Non-parametric Distribution Transformation, Q-Q Plot\")\n",
    "    plt.plot([0.05,0.25],[0.05,0.25],color = 'red',linestyle='dashed', alpha = 0.4)\n",
    "    plt.scatter(por_sample[data_index_sample-1],y_sample[data_index_sample-1],s = 50, c = 'green', edgecolor = 'black', alpha = 1.0, zorder=200) # plot the CDF points\n",
    "    plt.scatter(por_sample,y_sample,s = 20, c = 'green', edgecolor = 'black', alpha = 0.3, zorder=100) # plot the CDF points\n",
    "        \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot_s = widgets.interactive_output(run_plot_sample, {'data_index_sample':data_index_sample})\n",
    "#interactive_plot_sample.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Data Analytics Distribution Transformation Demonstration \n",
    "\n",
    "#### Michael Pyrcz, Professor, The University of Texas at Austin \n",
    "\n",
    "Select any data value and observe the distribution transform by mapping through cumulative probability.\n",
    "\n",
    "#### The Inputs\n",
    "\n",
    "* **data_index** - the data index from 1 to n in the sorted ascending order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca17aeca38884f218e1a44c56f3fb080",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                                                  Data Analytics, Distribution Tran…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9d8823cc470444a6b1062721a3b9cb8e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 640x480 with 3 Axes>', 'i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui_sample, interactive_plot_s)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To summarize let's look at a DataFrame with the original noisey sample and the transformed to match the original distribution.\n",
    "\n",
    "* we're making and showing a table of original values, $x_{\\beta}$ $\\forall$ $\\beta = 1, \\ldots, n_{sample}$, and the transformed values, $y_{\\beta}$ $\\forall$ $\\beta = 1, \\ldots, n_{sample}$.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>Transformed_Por</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>201.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.094549</td>\n",
       "      <td>0.400658</td>\n",
       "      <td>5263.542112</td>\n",
       "      <td>0.081044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>900.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.095709</td>\n",
       "      <td>1.280257</td>\n",
       "      <td>4573.656072</td>\n",
       "      <td>0.085867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>231.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.099580</td>\n",
       "      <td>5.584040</td>\n",
       "      <td>4919.074871</td>\n",
       "      <td>0.091834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>800.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.101670</td>\n",
       "      <td>7.739105</td>\n",
       "      <td>5274.532660</td>\n",
       "      <td>0.095628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>211.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.101765</td>\n",
       "      <td>6.368529</td>\n",
       "      <td>5725.334803</td>\n",
       "      <td>0.099378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.103027</td>\n",
       "      <td>2.446678</td>\n",
       "      <td>5201.637996</td>\n",
       "      <td>0.101987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>600.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.104890</td>\n",
       "      <td>12.384496</td>\n",
       "      <td>3595.586977</td>\n",
       "      <td>0.103970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>500.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.105033</td>\n",
       "      <td>6.312198</td>\n",
       "      <td>5515.918646</td>\n",
       "      <td>0.106704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>201.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109901</td>\n",
       "      <td>0.546396</td>\n",
       "      <td>5018.355476</td>\n",
       "      <td>0.108460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.113748</td>\n",
       "      <td>3.691908</td>\n",
       "      <td>5295.267191</td>\n",
       "      <td>0.111491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>251.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.120793</td>\n",
       "      <td>1.003374</td>\n",
       "      <td>5822.467914</td>\n",
       "      <td>0.113887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>900.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.124564</td>\n",
       "      <td>12.433996</td>\n",
       "      <td>6242.704810</td>\n",
       "      <td>0.117984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>600.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.128460</td>\n",
       "      <td>42.396044</td>\n",
       "      <td>4204.150893</td>\n",
       "      <td>0.121592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>955.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.174417</td>\n",
       "      <td>374.298925</td>\n",
       "      <td>3181.557281</td>\n",
       "      <td>0.127131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>955.0</td>\n",
       "      <td>529.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.181376</td>\n",
       "      <td>1113.971076</td>\n",
       "      <td>3177.635737</td>\n",
       "      <td>0.137062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>955.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.182492</td>\n",
       "      <td>74.215058</td>\n",
       "      <td>3386.182722</td>\n",
       "      <td>0.153759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>955.0</td>\n",
       "      <td>469.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.184461</td>\n",
       "      <td>26.197239</td>\n",
       "      <td>2889.196647</td>\n",
       "      <td>0.176536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>995.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.184864</td>\n",
       "      <td>460.494986</td>\n",
       "      <td>2792.804322</td>\n",
       "      <td>0.185270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>690.0</td>\n",
       "      <td>529.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.185477</td>\n",
       "      <td>316.905689</td>\n",
       "      <td>4271.013148</td>\n",
       "      <td>0.188136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>975.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.193729</td>\n",
       "      <td>69.336576</td>\n",
       "      <td>2493.128177</td>\n",
       "      <td>0.191655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>955.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.195955</td>\n",
       "      <td>45.002088</td>\n",
       "      <td>3394.563038</td>\n",
       "      <td>0.195998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1005.0</td>\n",
       "      <td>519.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.197107</td>\n",
       "      <td>54.667195</td>\n",
       "      <td>2577.714678</td>\n",
       "      <td>0.198182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>985.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.199185</td>\n",
       "      <td>73.133040</td>\n",
       "      <td>2672.294567</td>\n",
       "      <td>0.199315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>935.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.202592</td>\n",
       "      <td>523.287810</td>\n",
       "      <td>2579.032897</td>\n",
       "      <td>0.201943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>925.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.202754</td>\n",
       "      <td>211.163296</td>\n",
       "      <td>3442.885245</td>\n",
       "      <td>0.206934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>935.0</td>\n",
       "      <td>449.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.206140</td>\n",
       "      <td>368.507601</td>\n",
       "      <td>4249.477923</td>\n",
       "      <td>0.209051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>985.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.206985</td>\n",
       "      <td>68.276148</td>\n",
       "      <td>2547.526113</td>\n",
       "      <td>0.211638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>955.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.215542</td>\n",
       "      <td>1525.247066</td>\n",
       "      <td>2512.061434</td>\n",
       "      <td>0.215686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>975.0</td>\n",
       "      <td>529.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.228716</td>\n",
       "      <td>1548.094062</td>\n",
       "      <td>3167.185377</td>\n",
       "      <td>0.224214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>975.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.229653</td>\n",
       "      <td>21.109085</td>\n",
       "      <td>2412.875330</td>\n",
       "      <td>0.242298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          X      Y  Facies  Porosity         Perm           AI  \\\n",
       "207   201.0  426.0     0.0  0.094549     0.400658  5263.542112   \n",
       "80    900.0  100.0     0.0  0.095709     1.280257  4573.656072   \n",
       "210   231.0  426.0     0.0  0.099580     5.584040  4919.074871   \n",
       "71    800.0  100.0     0.0  0.101670     7.739105  5274.532660   \n",
       "226   211.0  396.0     0.0  0.101765     6.368529  5725.334803   \n",
       "3     100.0  600.0     0.0  0.103027     2.446678  5201.637996   \n",
       "47    600.0  700.0     0.0  0.104890    12.384496  3595.586977   \n",
       "41    500.0  400.0     0.0  0.105033     6.312198  5515.918646   \n",
       "189   201.0  456.0     0.0  0.109901     0.546396  5018.355476   \n",
       "5     100.0  400.0     0.0  0.113748     3.691908  5295.267191   \n",
       "218   251.0  416.0     0.0  0.120793     1.003374  5822.467914   \n",
       "72    900.0  900.0     0.0  0.124564    12.433996  6242.704810   \n",
       "53    600.0  100.0     1.0  0.128460    42.396044  4204.150893   \n",
       "93    955.0  549.0     1.0  0.174417   374.298925  3181.557281   \n",
       "111   955.0  529.0     1.0  0.181376  1113.971076  3177.635737   \n",
       "84    955.0  559.0     1.0  0.182492    74.215058  3386.182722   \n",
       "165   955.0  469.0     1.0  0.184461    26.197239  2889.196647   \n",
       "151   995.0  489.0     1.0  0.184864   460.494986  2792.804322   \n",
       "245   690.0  529.0     1.0  0.185477   316.905689  4271.013148   \n",
       "158   975.0  479.0     1.0  0.193729    69.336576  2493.128177   \n",
       "174   955.0  459.0     1.0  0.195955    45.002088  3394.563038   \n",
       "125  1005.0  519.0     1.0  0.197107    54.667195  2577.714678   \n",
       "150   985.0  489.0     1.0  0.199185    73.133040  2672.294567   \n",
       "136   935.0  499.0     1.0  0.202592   523.287810  2579.032897   \n",
       "99    925.0  539.0     1.0  0.202754   211.163296  3442.885245   \n",
       "181   935.0  449.0     1.0  0.206140   368.507601  4249.477923   \n",
       "141   985.0  499.0     1.0  0.206985    68.276148  2547.526113   \n",
       "129   955.0  509.0     1.0  0.215542  1525.247066  2512.061434   \n",
       "113   975.0  529.0     1.0  0.228716  1548.094062  3167.185377   \n",
       "149   975.0  489.0     1.0  0.229653    21.109085  2412.875330   \n",
       "\n",
       "     Transformed_Por  \n",
       "207         0.081044  \n",
       "80          0.085867  \n",
       "210         0.091834  \n",
       "71          0.095628  \n",
       "226         0.099378  \n",
       "3           0.101987  \n",
       "47          0.103970  \n",
       "41          0.106704  \n",
       "189         0.108460  \n",
       "5           0.111491  \n",
       "218         0.113887  \n",
       "72          0.117984  \n",
       "53          0.121592  \n",
       "93          0.127131  \n",
       "111         0.137062  \n",
       "84          0.153759  \n",
       "165         0.176536  \n",
       "151         0.185270  \n",
       "245         0.188136  \n",
       "158         0.191655  \n",
       "174         0.195998  \n",
       "125         0.198182  \n",
       "150         0.199315  \n",
       "136         0.201943  \n",
       "99          0.206934  \n",
       "181         0.209051  \n",
       "141         0.211638  \n",
       "129         0.215686  \n",
       "113         0.224214  \n",
       "149         0.242298  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample['Transformed_Por'] = y_sample\n",
    "df_sample.head(n=n_sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It would be straitforward to modify the code above to perform distribution transformations:\n",
    "\n",
    "* to a parametric distribution like Gaussian\n",
    "\n",
    "* to a non-parametric distribution from actual data (build a CDF and interpolate between the data samples)\n",
    "\n",
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of distribution transformations. \n",
    "\n",
    "I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available at [Python Demos](https://github.com/GeostatsGuy/PythonNumericalDemos) and a Python package for data analytics and geostatistics at [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy). \n",
    "  \n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Professor, Cockrell School of Engineering and The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
